For our experiments, we have used the Librispeech dataset [1]} which is a diverse collection of audio book data along with the ground text. It has around 1000 Hours of audio recordings with different levels of complexity. We pass these audio clips through an ASR system to get its transcripts and the WER is calculated by comparing it with the ground text. This paper reports findings in the experiments run with Google Cloud's Speech-to-Text APIhttps://cloud.google.com/speech-to-text.. We chose this commercial ASR system, rather than reporting results on an internal ASR system, since it's easily accessible through the google-api and the results are reproducible. For our experiments, we have used the 10 and 100 hour datasets and made a 60:20:20 split into train, dev and test sets for each dataset. As can be seen in Table 1 of Section B of the Appendix, the characteristics of the 100 and 10 hour datasets are quite different. However, within a dataset, the train, dev and test sets have similar distributions of WER and other characteristics. Table  lists a few examples with each row having the ground text, the transcript obtained from Google Cloud's Speech-to-Text API, the True WER and the WER predicted by our proposed model.
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